3. 训练Word2Vec模型 训练Word2Vec模型非常简单,使用gensim库中的Word2Vec类即可实现。 fromgensim.modelsimportWord2Vec# 使用分词后的数据训练Word2Vec模型model=Word2Vec([tokens],vector_size=100,window=5,min_count=1,sg=0)# 查看词向量word_vector=model.wv['python']# 获取'python'的词向量print(word...
intersection = len(set1.intersection(set2)) union = len(set1.union(set2)) return intersection / union text1 = set("This is the first document.".split()) text2 = set("This document is the second document.".split()) similarity = jaccard_similarity(text1, text2) print(similarity) 编辑...
print(lines[0:5])#预览前5行分词结果 from gensim.models.word2vec import Word2Vec # 调用Word2Vec训练 参数:size: 词向量维度;window: 上下文的宽度,min_count为考虑计算的单词的最低词频阈值 model = Word2Vec(lines,vector_size = 20, window = 2 , min_count = 3, epochs=7, negative=10,sg=1...
sentences = word2vec.Text8Corpus("files/data/python32-data/word.txt") # 加载分词语料 # 训练skip-gram模型,使用vector_size参数替代size model = word2vec.Word2Vec(sentences, vector_size=200) # 默认window=5 print("输出模型", model) # 计算两个单词的相似度 try: y1 = model.wv.similarity("...
self.vocab = {} # mapping from a word (string) to a Vocab object self.index2word = [] # map from a word's matrix index (int) to word (string) self.sg = int(sg) self.cum_table = None # for negative sampling self.vector_size = int(size) ...
wv.vector_size)# # 获取某个词的词向量, 先判断再获取noun1 ='手机'ifnoun1inmodel.wv.vocab:print(model.wv[noun1])# 计算两个词的相似度(余弦距离),结果越大越相似noun2 ='电池'noun3 ='电量'noun4 ='续航'print(model.wv.similarity(noun1, noun2))print(model.wv.similarity(noun3, noun2)...
model = Word2Vec(sentences, vector_size=100, window=5, min_count=1, workers=4) word_vector = model.wv['machine'] ``` 3. GloVe GloVe是一种基于全局词频统计的词嵌入方法,通过最小化词语共现矩阵的损失函数来学习词向量。 ```python
from gensim.models import Word2Vec sentences = [["this", "is", "the", "first", "sentence"], ["this", "is", "the", "second", "sentence"], ["is", "this", "the", "third", "sentence"]] model = Word2Vec(sentences, vector_size=100, window=5, min_count=1, sg=0) ...
Word2Vec是由Mikolov等人在一篇名为“Efficient Estimation of Word Representations in Vector Space”的论文中发表的算法。这篇论文值得一读,虽然在本文中,我们将从头开始在PyTorch中构建它。 简而言之,Word2Vec使用一个单隐藏层的人工神经网络来学习稠密的词向量嵌入。这些词嵌入使我们能够识别具有相似语义含义的单词...
model = Word2Vec(sentences, vector_size=100, window=5, min_count=1, workers=4) word_vector = model.wv['machine'] ``` 3. GloVe GloVe是一种基于全局词频统计的词嵌入方法,通过最小化词语共现矩阵的损失函数来学习词向量。 ```python